Recently, considerably progress has been achieved in the use of neural network methodologies for both diagnostic and control applications of nonlinear dynamical systems. This progress is due in part to the use of context sensitive neural network architectures (as in recurrent networks) and in part to improved training methodologies (as with multistream training techniques). The bulk of previous efforts used static or feedforward networks, which were plagued by slow adaptation and large error rates. Architecturally, recurrent neural networks are simple extensions of feedforward networks where the network's neuron node outputs are no longer a function of their current inputs, but also of the recent time history of inputs via time-lagged connections.
In the automotive sector to date, this recurrent neural network formalism has been successfully applied and reported in the literature for several engine subsystems. These include the idle speed problem and the antilock brake problem (control problems) and the misfire detection problem (a diagnostic problem). In either case, the recurrent neuromorphic methodologies developed were trained to detect, identify and/or control improper events in an operating internal combustion engine. In order to utilize information from sensors now in production use, the diagnostic and control operations are based upon the temporal analysis of existing sensor outputs or dynamics. It has been demonstrated that the diagnostic and control tasks can be accomplished by the use of trainable classifiers of suitable capacity. These trainable classifiers, however, are based upon systems which require considerable computational resources and as such require dedicated hardware implementations in order to meet the real-time on-board computations requirements. While there exist a number of commercially available neural hardware implementations, none meet the specific design requirements needed for large scale commercial deployment in the automotive sector.